By Topic

How robust is the SVM wound segmentation?

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Marina Kolesnik ; Fraunhofer Institute for Applied Information Technology, Schloss Birlinghoven, 53604 Sankt Augustin, GERMANY. Tel. +49-2241 14 3421, Fax: +49-2241 14 1506, E-mail: ; Ales Fexa

This paper investigates the robustness of automatic wound segmentation. The work builds upon an automatic segmentation procedure by the support vector machine (SVM)-classifier presented in [M. Kolesnik et al. (2004), (2005)]. Here we extend the procedure by incorporating textural features and the deformable snake adjustment to refine SVM-generated wound boundary. The robustness of SVM-based segmentation is tested against different feature spaces using a long sample of training images featuring a broad variety of wounds' appearance. Recommendations drawn from these experiments provide a useful guideline for the development of a software support system for the visual monitoring of chronic wounds in wound care units

Published in:

Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006

Date of Conference:

7-9 June 2006